Poly-process Query Processing via Bridge Entity Segmentation
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Solution Overview
Problem
Existing natural language processing systems struggle to accurately process poly-process queries that span multiple business processes and databases, often providing inaccurate results or requiring significant user effort due to their design limitations in handling queries with attributes across multiple processes.
Innovation Solution
The system identifies a bridge entity within a natural language query, splits it into sub-queries, processes each sub-query individually, and combines the results using the bridge entity to generate a final accurate output, improving the handling of queries that span multiple business processes and databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing natural language processing systems process poly-process queries using standard single-process query methods, then the system design remains simple, but the query processing accuracy deteriorates and produces inaccurate results
Solution Approach 1:
The system segments a poly-process natural language query into multiple single-process sub-queries by identifying a bridge entity that connects different business processes. Each sub-query is then processed independently through its respective business process, and the results are combined to form the final answer. This segmentation allows accurate handling of complex queries while maintaining relatively simple individual processing components.
2Measurement precision
If the system automatically identifies and processes poly-process queries by splitting into sub-queries, then the query processing accuracy improves, but the processing time and computational effort increase
Solution Approach 1:
The system performs preliminary analysis by identifying the bridge entity and determining whether a query is poly-process before actual query execution. This preliminary classification allows the system to prepare the query decomposition strategy in advance, enabling more efficient processing by avoiding unnecessary complex analysis for simple single-process queries and streamlining the sub-query generation process for poly-process queries.
3Adaptability or versatility
If the system handles queries spanning multiple business processes and databases, then the query versatility improves, but the system complexity increases
Solution Approach 1:
The bridge entity serves as an intermediary that connects different business processes and databases. By identifying this common entity that appears across multiple processes, the system can coordinate queries across process boundaries without requiring complex direct integration between all processes. The bridge entity acts as a natural join point that simplifies the architecture for handling cross-process queries.
4Device complexity
If the system requires user effort to manually break down complex queries, then the processing complexity for the system decreases, but the ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically analyzing the natural language query, identifying bridge entities, determining poly-process characteristics, and decomposing the query into appropriate sub-queries without requiring user intervention. This automation eliminates the need for users to manually break down complex queries or understand the underlying system architecture, significantly improving ease of operation while maintaining systematic processing complexity at acceptable levels.
Data Source
AI summary
An embodiment for identifying and processing poly-process natural language queries may include receiving a natural language query. The embodiment may also automatically identify a bridge entity in the received natural language query. The embodiment may also automatically determine whether the received natural language query is a poly-process query. The embodiment may further include, in response to identifying that the received natural language query is the poly-process query, automatically generating sub-queries for each process in the poly-process query and generate results for each sub-query. The embodiment may also automatically combining the results of each sub-query using the bridge entity to output a combined result. The embodiment may further include automatically generating a modified sub-query for post-processing of the combined result. The embodiment may also automatically process the modified sub-query to generate a final query result for the received natural language query.